A hybrid method of GA and BP for short-term economic dispatch of hydrothermal power systems
Mathematics and Computers in Simulation - Special issue from the IMACS/IFAC international symposium on soft computing methods and applications: “SOFTCOM '99” (held in Athens, Greece)
A review of genetic algorithms applied to training radial basis function networks
Neural Computing and Applications
Neural Computing and Applications
Optimum design of structures by an improved genetic algorithm using neural networks
Advances in Engineering Software - Selected papers from civil-comp 2003 and AlCivil-comp 2003
Particle swarm optimization neural network and its application in soft-sensing modeling
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part II
Evolving artificial neural network ensembles
IEEE Computational Intelligence Magazine
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A back-propagation (BP) neural network has good self-learning, self-adapting and generalization ability, but it may easily get stuck in a local minimum, and has a poor rate of convergence. Therefore, a method to optimize a BP algorithm based on a genetic algorithm (GA) is proposed to speed the training of BP, and to overcome BP's disadvantage of being easily stuck in a local minimum. The UCI data set is used here for experimental analysis and the experimental result shows that, compared with the BP algorithm and a method that only uses GA to learn the connection weights, our method that combines GA and BP to train the neural network works better; is less easily stuck in a local minimum; the trained network has a better generalization ability; and it has a good stabilization performance.